Overview

Dataset statistics

Number of variables21
Number of observations21613
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical4

Dataset

DescriptionPredict house price using regression.
URL
Copyright(c) Mr. Eslam Fouad 2023

Alerts

date has a high cardinality: 372 distinct valuesHigh cardinality
price is highly overall correlated with sqft_living and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 5 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with price and 6 other fieldsHigh correlation
sqft_above is highly overall correlated with price and 6 other fieldsHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with price and 4 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
waterfront is highly overall correlated with viewHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
sqft_basement has 13126 (60.7%) zerosZeros
yr_renovated has 20699 (95.8%) zerosZeros

Reproduction

Analysis started2023-06-16 12:34:59.219727
Analysis finished2023-06-16 12:36:40.526922
Duration1 minute and 41.31 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct21436
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5803015 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:40.787343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1248034 × 108
Q12.1230492 × 109
median3.9049304 × 109
Q37.3089004 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858513 × 109

Descriptive statistics

Standard deviation2.8765656 × 109
Coefficient of variation (CV)0.62802974
Kurtosis-1.2605419
Mean4.5803015 × 109
Median Absolute Deviation (MAD)2.4025301 × 109
Skewness0.24332855
Sum9.8994057 × 1013
Variance8.2746295 × 1018
MonotonicityNot monotonic
2023-06-16T12:36:41.273970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795000620 3
 
< 0.1%
8651510380 2
 
< 0.1%
2568300045 2
 
< 0.1%
9353300600 2
 
< 0.1%
4139480200 2
 
< 0.1%
1954420170 2
 
< 0.1%
6381500170 2
 
< 0.1%
7167000040 2
 
< 0.1%
9407110710 2
 
< 0.1%
1000102 2
 
< 0.1%
Other values (21426) 21592
99.9%
ValueCountFrequency (%)
1000102 2
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Categorical

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
20140623T000000
 
142
20140626T000000
 
131
20140625T000000
 
131
20140708T000000
 
127
20150427T000000
 
126
Other values (367)
20956 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters324195
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row20141013T000000
2nd row20141209T000000
3rd row20150225T000000
4th row20141209T000000
5th row20150218T000000

Common Values

ValueCountFrequency (%)
20140623T000000 142
 
0.7%
20140626T000000 131
 
0.6%
20140625T000000 131
 
0.6%
20140708T000000 127
 
0.6%
20150427T000000 126
 
0.6%
20150325T000000 123
 
0.6%
20150422T000000 121
 
0.6%
20140709T000000 121
 
0.6%
20150428T000000 121
 
0.6%
20150414T000000 121
 
0.6%
Other values (362) 20349
94.2%

Length

2023-06-16T12:36:41.706614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20140623t000000 142
 
0.7%
20140625t000000 131
 
0.6%
20140626t000000 131
 
0.6%
20140708t000000 127
 
0.6%
20150427t000000 126
 
0.6%
20150325t000000 123
 
0.6%
20150422t000000 121
 
0.6%
20140709t000000 121
 
0.6%
20150428t000000 121
 
0.6%
20150414t000000 121
 
0.6%
Other values (362) 20349
94.2%

Most occurring characters

ValueCountFrequency (%)
0 178543
55.1%
1 37980
 
11.7%
2 33852
 
10.4%
T 21613
 
6.7%
4 18999
 
5.9%
5 11564
 
3.6%
3 5066
 
1.6%
7 4354
 
1.3%
6 4328
 
1.3%
8 4009
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 302582
93.3%
Uppercase Letter 21613
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178543
59.0%
1 37980
 
12.6%
2 33852
 
11.2%
4 18999
 
6.3%
5 11564
 
3.8%
3 5066
 
1.7%
7 4354
 
1.4%
6 4328
 
1.4%
8 4009
 
1.3%
9 3887
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
T 21613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302582
93.3%
Latin 21613
 
6.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178543
59.0%
1 37980
 
12.6%
2 33852
 
11.2%
4 18999
 
6.3%
5 11564
 
3.8%
3 5066
 
1.7%
7 4354
 
1.4%
6 4328
 
1.4%
8 4009
 
1.3%
9 3887
 
1.3%
Latin
ValueCountFrequency (%)
T 21613
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178543
55.1%
1 37980
 
11.7%
2 33852
 
10.4%
T 21613
 
6.7%
4 18999
 
5.9%
5 11564
 
3.6%
3 5066
 
1.6%
7 4354
 
1.3%
6 4328
 
1.3%
8 4009
 
1.2%

price
Real number (ℝ)

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540088.14
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:42.131372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1156480
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367127.2
Coefficient of variation (CV)0.67975423
Kurtosis34.58554
Mean540088.14
Median Absolute Deviation (MAD)150000
Skewness4.0240691
Sum1.1672925 × 1010
Variance1.3478238 × 1011
MonotonicityNot monotonic
2023-06-16T12:36:42.599624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 172
 
0.8%
450000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (4018) 20113
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7062500 1
< 0.1%
6885000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110800 1
< 0.1%
4668000 1
< 0.1%
4500000 1
< 0.1%
4489000 1
< 0.1%

bedrooms
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3708416
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:43.020634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93006183
Coefficient of variation (CV)0.27591383
Kurtosis49.063653
Mean3.3708416
Median Absolute Deviation (MAD)1
Skewness1.9742995
Sum72854
Variance0.86501501
MonotonicityNot monotonic
2023-06-16T12:36:43.388417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.8%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 199
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 199
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.8%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.8%
3 9824
45.5%

bathrooms
Real number (ℝ)

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147573
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:43.814674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77016316
Coefficient of variation (CV)0.36418512
Kurtosis1.2799024
Mean2.1147573
Median Absolute Deviation (MAD)0.5
Skewness0.51110757
Sum45706.25
Variance0.59315129
MonotonicityNot monotonic
2023-06-16T12:36:44.238283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5380
24.9%
1 3852
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1446
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (20) 652
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 72
 
0.3%
1 3852
17.8%
1.25 9
 
< 0.1%
1.5 1446
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5380
24.9%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8997
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:44.677814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.4409
Coefficient of variation (CV)0.44157941
Kurtosis5.243093
Mean2079.8997
Median Absolute Deviation (MAD)540
Skewness1.4715554
Sum44952873
Variance843533.68
MonotonicityNot monotonic
2023-06-16T12:36:45.155764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1028) 20318
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.968
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:45.621856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.512
Coefficient of variation (CV)2.7418151
Kurtosis285.07782
Mean15106.968
Median Absolute Deviation (MAD)2618
Skewness13.060019
Sum3.2650689 × 108
Variance1.7156588 × 109
MonotonicityNot monotonic
2023-06-16T12:36:46.024409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 120
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9772) 19818
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494309
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:46.409874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399889
Coefficient of variation (CV)0.36136361
Kurtosis-0.48472294
Mean1.494309
Median Absolute Deviation (MAD)0.5
Skewness0.61617672
Sum32296.5
Variance0.29158801
MonotonicityNot monotonic
2023-06-16T12:36:46.795201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10680
49.4%
2 8241
38.1%
1.5 1910
 
8.8%
3 613
 
2.8%
2.5 161
 
0.7%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10680
49.4%
1.5 1910
 
8.8%
2 8241
38.1%
2.5 161
 
0.7%
3 613
 
2.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 613
 
2.8%
2.5 161
 
0.7%
2 8241
38.1%
1.5 1910
 
8.8%
1 10680
49.4%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Length

2023-06-16T12:36:47.203277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-16T12:36:47.612721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Length

2023-06-16T12:36:47.984887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-16T12:36:48.413100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Length

2023-06-16T12:36:48.822079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-16T12:36:49.252783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

grade
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6568732
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:49.611565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1754588
Coefficient of variation (CV)0.15351681
Kurtosis1.1909321
Mean7.6568732
Median Absolute Deviation (MAD)1
Skewness0.7711032
Sum165488
Variance1.3817033
MonotonicityNot monotonic
2023-06-16T12:36:50.000596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8981
41.6%
8 6068
28.1%
9 2615
 
12.1%
6 2038
 
9.4%
10 1134
 
5.2%
11 399
 
1.8%
5 242
 
1.1%
12 90
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8981
41.6%
8 6068
28.1%
9 2615
 
12.1%
10 1134
 
5.2%
11 399
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 90
 
0.4%
11 399
 
1.8%
10 1134
 
5.2%
9 2615
 
12.1%
8 6068
28.1%
7 8981
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.3907
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:50.417918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.09098
Coefficient of variation (CV)0.46303695
Kurtosis3.4023036
Mean1788.3907
Median Absolute Deviation (MAD)450
Skewness1.4466645
Sum38652488
Variance685734.67
MonotonicityNot monotonic
2023-06-16T12:36:50.872930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (936) 19724
91.3%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.50905
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:51.314198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.57504
Coefficient of variation (CV)1.5182206
Kurtosis2.7155742
Mean291.50905
Median Absolute Deviation (MAD)0
Skewness1.5779651
Sum6300385
Variance195872.67
MonotonicityNot monotonic
2023-06-16T12:36:51.777812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13126
60.7%
600 221
 
1.0%
700 218
 
1.0%
500 214
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 149
 
0.7%
900 144
 
0.7%
300 142
 
0.7%
200 108
 
0.5%
Other values (296) 6901
31.9%
ValueCountFrequency (%)
0 13126
60.7%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0051
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:52.222293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.373411
Coefficient of variation (CV)0.014902757
Kurtosis-0.6574075
Mean1971.0051
Median Absolute Deviation (MAD)23
Skewness-0.4698054
Sum42599334
Variance862.79726
MonotonicityNot monotonic
2023-06-16T12:36:52.693028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 422
 
2.0%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17326
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 454
2.1%

yr_renovated
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.402258
Minimum0
Maximum2015
Zeros20699
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:53.161515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.67924
Coefficient of variation (CV)4.7591054
Kurtosis18.701152
Mean84.402258
Median Absolute Deviation (MAD)0
Skewness4.5494934
Sum1824186
Variance161346.21
MonotonicityNot monotonic
2023-06-16T12:36:53.622210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20699
95.8%
2014 91
 
0.4%
2013 37
 
0.2%
2003 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 35
 
0.2%
2004 26
 
0.1%
1990 25
 
0.1%
2006 24
 
0.1%
Other values (60) 570
 
2.6%
ValueCountFrequency (%)
0 20699
95.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 16
 
0.1%
2014 91
0.4%
2013 37
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 18
 
0.1%
2009 22
 
0.1%
2008 18
 
0.1%
2007 35
 
0.2%
2006 24
 
0.1%

zipcode
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.94
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:54.107175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.505026
Coefficient of variation (CV)0.00054553579
Kurtosis-0.85347887
Mean98077.94
Median Absolute Deviation (MAD)42
Skewness0.40566121
Sum2.1197585 × 109
Variance2862.7878
MonotonicityNot monotonic
2023-06-16T12:36:54.589006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 602
 
2.8%
98038 590
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 548
 
2.5%
98034 545
 
2.5%
98118 508
 
2.4%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16113
74.6%
ValueCountFrequency (%)
98001 362
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 141
 
0.7%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560053
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:55.077347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.13856371
Coefficient of variation (CV)0.0029134474
Kurtosis-0.676313
Mean47.560053
Median Absolute Deviation (MAD)0.1049
Skewness-0.48527048
Sum1027915.4
Variance0.019199902
MonotonicityNot monotonic
2023-06-16T12:36:56.211067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.6624 17
 
0.1%
47.5322 17
 
0.1%
47.6846 17
 
0.1%
47.5491 17
 
0.1%
47.6955 16
 
0.1%
47.6886 16
 
0.1%
47.6711 16
 
0.1%
47.5402 15
 
0.1%
47.6842 15
 
0.1%
47.6904 15
 
0.1%
Other values (5024) 21452
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21613
Negative (%)100.0%
Memory size169.0 KiB
2023-06-16T12:36:56.662680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14082834
Coefficient of variation (CV)-0.0011523104
Kurtosis1.0495009
Mean-122.2139
Median Absolute Deviation (MAD)0.101
Skewness0.88505298
Sum-2641408.9
Variance0.019832622
MonotonicityNot monotonic
2023-06-16T12:36:57.133321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 116
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20601
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.5525
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:57.571779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.3913
Coefficient of variation (CV)0.34501545
Kurtosis1.5970958
Mean1986.5525
Median Absolute Deviation (MAD)410
Skewness1.1081813
Sum42935359
Variance469761.24
MonotonicityNot monotonic
2023-06-16T12:36:58.002867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 181
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1720 166
 
0.8%
1800 166
 
0.8%
1620 165
 
0.8%
Other values (767) 19849
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12768.456
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2023-06-16T12:36:58.454954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999.2
Q15100
median7620
Q310083
95-th percentile37062.8
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27304.18
Coefficient of variation (CV)2.1384089
Kurtosis150.76311
Mean12768.456
Median Absolute Deviation (MAD)2505
Skewness9.5067432
Sum2.7596463 × 108
Variance7.4551823 × 108
MonotonicityNot monotonic
2023-06-16T12:36:58.899348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 357
 
1.7%
6000 289
 
1.3%
7200 211
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
3600 111
 
0.5%
4500 111
 
0.5%
5100 109
 
0.5%
Other values (8679) 19595
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2023-06-16T12:36:35.209624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:09.704589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:14.292418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:18.249071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:22.593729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:28.922602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:34.962100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:40.382779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:44.535951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:48.313734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:52.626510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:58.844409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:04.855190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:10.891721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:17.039729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:22.943468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:29.371284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:35:22.941340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:35:40.595549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:44.755514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:36:11.237580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:36:23.285500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:36:35.901884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:10.414700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:14.765716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:18.709775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:23.303005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:29.640514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:35:45.004926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:48.769727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:53.322660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:36:11.609387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:17.736206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:23.649001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:30.078731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:36.120438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:10.753675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:15.001504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-16T12:36:37.867019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:12.701073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:16.853482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:20.700315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:26.779207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:32.825306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:38.641223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:43.153087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:46.982926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:50.739775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:56.357222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:02.725343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:08.764148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:14.862196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:20.836683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:27.283503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:33.144393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:38.115791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:12.929231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:17.097798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:20.936713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:27.148885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:33.202728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:38.945068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:43.381436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:47.215662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:50.982267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:56.678550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:03.096150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:09.132150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:15.235485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:21.205047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:27.642218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:33.505791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:38.354065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:13.168365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:17.352829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:21.253120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:27.523492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:33.570760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:39.315088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:43.622632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:47.448292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:51.293115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:57.041787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:03.470003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:09.508892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:15.610676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:21.571661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:28.016281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:33.888452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:38.570338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:13.399011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:17.579804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:21.595527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:27.881455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:33.931108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:39.671673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:43.852001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:47.672241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:51.632750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:57.390409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:03.828596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:09.860957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:15.984765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:21.923323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:28.366332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:34.225826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:38.788426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:13.622154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:17.808481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:21.937844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:28.231493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:34.280461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:39.957200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:44.086323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:47.891211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:51.969219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:57.726849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:04.175269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:10.209829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:16.343634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:22.269893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:28.697271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:34.525986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:39.000151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:13.837096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:18.037575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:22.269387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:28.581383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:34.623839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:40.175398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:44.313690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:48.108166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:52.301862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:35:58.519530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:04.518936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:10.555228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:16.698188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:22.613868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:29.036337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-16T12:36:34.873626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-16T12:36:59.285211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorsgradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15waterfrontviewcondition
id1.0000.0040.0060.0150.002-0.1170.0190.0200.0040.0010.027-0.017-0.005-0.0040.007-0.000-0.1150.0060.0290.030
price0.0041.0000.3450.4970.6440.0750.3220.6580.5420.2520.1020.102-0.0090.4560.0640.5720.0630.3200.2080.023
bedrooms0.0060.3451.0000.5210.6470.2170.2280.3810.5400.2300.1800.017-0.167-0.0210.1910.4440.2020.0000.0380.024
bathrooms0.0150.4970.5211.0000.7460.0690.5470.6580.6910.1920.5670.043-0.2050.0080.2620.5700.0630.1020.1140.130
sqft_living0.0020.6440.6470.7461.0000.3040.4010.7160.8440.3280.3520.053-0.2070.0310.2850.7470.2840.1400.1490.060
sqft_lot-0.1170.0750.2170.0690.3041.000-0.2340.1520.2720.037-0.0380.009-0.319-0.1220.3710.3600.9220.0140.0400.039
floors0.0190.3220.2280.5470.401-0.2341.0000.5020.599-0.2720.5520.013-0.0610.0250.1490.305-0.2310.0220.0240.179
grade0.0200.6580.3810.6580.7160.1520.5021.0000.7120.0930.5010.016-0.1820.1040.2230.6630.1560.1180.1430.154
sqft_above0.0040.5420.5400.6910.8440.2720.5990.7121.000-0.1660.4720.031-0.279-0.0260.3850.6970.2540.0830.0890.107
sqft_basement0.0010.2520.2300.1920.3280.037-0.2720.093-0.1661.000-0.1780.0630.1150.116-0.2000.1300.0310.1340.1590.094
yr_built0.0270.1020.1800.5670.352-0.0380.5520.5010.472-0.1781.000-0.215-0.317-0.1260.4130.336-0.0160.0320.0410.248
yr_renovated-0.0170.1020.0170.0430.0530.0090.0130.0160.0310.063-0.2151.0000.0620.025-0.075-0.0060.0090.0920.1090.067
zipcode-0.005-0.009-0.167-0.205-0.207-0.319-0.061-0.182-0.2790.115-0.3170.0621.0000.250-0.577-0.287-0.3260.0790.0740.074
lat-0.0040.456-0.0210.0080.031-0.1220.0250.104-0.0260.116-0.1260.0250.2501.000-0.1430.028-0.1170.0340.0680.058
long0.0070.0640.1910.2620.2850.3710.1490.2230.385-0.2000.413-0.075-0.577-0.1431.0000.3800.3730.0960.0850.081
sqft_living15-0.0000.5720.4440.5700.7470.3600.3050.6630.6970.1300.336-0.006-0.2870.0280.3801.0000.3660.0890.1470.062
sqft_lot15-0.1150.0630.2020.0630.2840.922-0.2310.1560.2540.031-0.0160.009-0.326-0.1170.3730.3661.0000.0000.0350.013
waterfront0.0060.3200.0000.1020.1400.0140.0220.1180.0830.1340.0320.0920.0790.0340.0960.0890.0001.0000.5920.017
view0.0290.2080.0380.1140.1490.0400.0240.1430.0890.1590.0410.1090.0740.0680.0850.1470.0350.5921.0000.025
condition0.0300.0230.0240.1300.0600.0390.1790.1540.1070.0940.2480.0670.0740.0580.0810.0620.0130.0170.0251.000

Missing values

2023-06-16T12:36:39.355535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-16T12:36:40.041540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052020141013T000000221900.031.00118056501.0003711800195509817847.5112-122.25713405650
1641410019220141209T000000538000.032.25257072422.000372170400195119919812547.7210-122.31916907639
2563150040020150225T000000180000.021.00770100001.000367700193309802847.7379-122.23327208062
3248720087520141209T000000604000.043.00196050001.000571050910196509813647.5208-122.39313605000
4195440051020150218T000000510000.032.00168080801.0003816800198709807447.6168-122.04518007503
5723755031020140512T0000001225000.044.5054201019301.00031138901530200109805347.6561-122.0054760101930
6132140006020140627T000000257500.032.25171568192.0003717150199509800347.3097-122.32722386819
7200800027020150115T000000291850.031.50106097111.0003710600196309819847.4095-122.31516509711
8241460012620150415T000000229500.031.00178074701.000371050730196009814647.5123-122.33717808113
9379350016020150312T000000323000.032.50189065602.0003718900200309803847.3684-122.03123907570
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
21603785214004020140825T000000507250.032.50227055362.0003822700200309806547.5389-121.88122705731
21604983420136720150126T000000429000.032.00149011263.0003814900201409814447.5699-122.28814001230
21605344890021020141014T000000610685.042.50252060232.0003925200201409805647.5137-122.16725206023
21606793600042920150326T0000001007500.043.50351072002.000392600910200909813647.5537-122.39820506200
21607299780002120150219T000000475000.032.50131012942.000381180130200809811647.5773-122.40913301265
2160826300001820140521T000000360000.032.50153011313.0003815300200909810347.6993-122.34615301509
21609660006012020150223T000000400000.042.50231058132.0003823100201409814647.5107-122.36218307200
21610152330014120140623T000000402101.020.75102013502.0003710200200909814447.5944-122.29910202007
2161129131010020150116T000000400000.032.50160023882.0003816000200409802747.5345-122.06914101287
21612152330015720141015T000000325000.020.75102010762.0003710200200809814447.5941-122.29910201357